Identifying errors in medical data

Abstract

A computer processor may receive medical data including a report and an image. The computer processor may analyze the report using natural language processing to identify a condition and a corresponding criterion. The computer processor may also analyze the image using an image processing model to generate an image analysis. The computer processor may determine whether the report has a potential problem by comparing the image analysis to the criterion.

What is claimed is: 
     
       1. A system for identifying errors in medical data, the system comprising:
 a processor; 
 a memory containing one or more modules, wherein the one or more modules, when executed, cause the processor to:
 receive medical data comprising a report and an image; 
 analyze the report using natural language processing (NLP) to identify a condition and a criterion, wherein the condition is a medical condition, and wherein the criterion includes diagnostic information corresponding to the condition; 
 generate an image analysis by analyzing the image using an image processing model; and 
 determine whether the report has a potential problem by comparing the criterion to the image analysis. 
 
 
     
     
       2. The system of  claim 1 , wherein there are a plurality of image processing models, each image processing model of the plurality of image processing models corresponding to a group of medical conditions, and wherein the image processing model used to generate the image analysis is selected from the plurality of image processing models based on the condition. 
     
     
       3. The system of  claim 1 , wherein the one or more modules, when executed, further cause the processor to:
 provide, in response to determining that the report has a potential problem, a notification indicating the potential problem. 
 
     
     
       4. The system of  claim 1 , wherein the one or more modules, when executed, further cause the processor to:
 perform, prior to analyzing the report using natural language processing, optical character recognition on the report. 
 
     
     
       5. A computer program product for identifying errors in medical data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instruction executable by a processor to cause the processor to perform a method comprising:
 receiving medical data comprising a report and an image; 
 analyzing the report using natural language processing (NLP) to identify a condition and a criterion, wherein the condition is a medical condition, and wherein the criterion includes diagnostic information corresponding to the condition; 
 generating an image analysis by analyzing the image using an image processing model; and 
 determining whether the report has a potential problem by comparing the criterion to the image analysis. 
 
     
     
       6. The computer program product of  claim 5 , wherein there are a plurality of image processing models, each image processing model of the plurality of image processing models corresponding to a group of medical conditions, and wherein the image processing model used to generate the image analysis is selected from the plurality of image processing models based on the condition. 
     
     
       7. The computer program product of  claim 5 , the method performed by the processor further comprising:
 providing, in response to determining that the report has a potential problem, a notification indicating the potential problem. 
 
     
     
       8. The computer program product of  claim 7 , wherein the notification is provided to a remote device over a network. 
     
     
       9. The computer program product of  claim 5 , wherein the report has a potential problem due to a clerical error. 
     
     
       10. The system of  claim 2 , wherein the plurality of image processing models includes a fracture analysis model and a tumor analysis model. 
     
     
       11. The system of  claim 10 , wherein the fracture analysis model is configured to analyze X-Ray images to identify a presence, size, type, and location of bone fractures, and wherein the tumor analysis model is configured to analyze computerized axial tomography (CAT) scan images to identify the size and location of tumors. 
     
     
       12. The system of  claim 10 , wherein the plurality of image processing models further include a model for detecting signs of diabetes, a model for detecting problems with the structure of a heart, a model for detecting torn ligaments, and a model for detecting bulging discs. 
     
     
       13. The system of  claim 1 , wherein the image analysis includes an image condition and an image criterion, wherein the image condition is a medical condition identified in the image, and the image criterion is diagnostic information about the image condition. 
     
     
       14. The system of  claim 13 , wherein determining whether the report has a potential problem includes comparing the condition to the image condition and the criterion to the image criterion. 
     
     
       15. The computer program product of  claim 5 , wherein the criterion includes a severity of the condition. 
     
     
       16. The computer program product of  claim 5 , wherein the condition is a bone fracture, and wherein the criterion is a Gustilo grade of the bone fracture. 
     
     
       17. The computer program product of  claim 5 , wherein the plurality of image processing models includes a fracture analysis model and a tumor analysis model. 
     
     
       18. The computer program product of  claim 17 , wherein the fracture analysis model is configured to analyze X-Ray images to identify a presence, size, type, and location of bone fractures, and wherein the tumor analysis model is configured to analyze computerized axial tomography (CAT) scan images to identify the size and location of tumors. 
     
     
       19. The computer program product of  claim 5 , wherein the condition is a bone fracture, wherein the criterion is a size of the bone fracture in the report, wherein the image analysis includes a size of the bone fracture identified in the image, and wherein the determining whether the report has a potential problem includes:
 determining a difference between the size of the bone fracture identified from the report and a size of the bone fracture identified in the image; 
 comparing the difference to a threshold; 
 determining that the difference is greater than the threshold; and 
 determining, based on the difference being greater than the threshold, that the report has a potential problem. 
 
     
     
       20. A computer program product for identifying errors in medical data, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instruction executable by a processor to cause the processor to perform a method comprising:
 receiving medical data of a patient, the medical data comprising a medical report and an image; 
 converting the medical report into machine-encoded text using optical character recognition; 
 analyzing, by a processor, the converted medical report using natural language processing (NLP) to identify a condition and a criterion, wherein the condition is a medical condition, and wherein the criterion includes a severity that corresponds to the condition; 
 determining, based on the identified condition and from a plurality of image processing models, a particular image processing model that corresponds to the identified condition, wherein the plurality of image processing models includes a fracture analysis model and a tumor analysis model; 
 generating, by the processor, an image analysis by analyzing the image using the particular image processing model, the image analysis including a an image condition and an image criterion, wherein the image condition is a medical condition identified in the image, and the image criterion is a severity of the image condition; 
 determining that the condition and the image condition match; 
 comparing, in response to determining that the condition and the image condition match, the criterion to the image criterion; 
 determining that the criterion and the image criterion do not match; and 
 transmitting, in response to determining that the criterion and the image criterion do not match, a notification that the report has a potential problem along with the report to a user.